Digital Forensics Triage Classification Model using Hybrid Learning Approaches

The Internet and the accessibility of gadgets with connectivity have resulted in the global spread of cyber threats and cybercrime, posing significant hurdles for digital forensics. Consequently, the volume of information that may need to be investigated is growing, necessitating the development of new forensic technologies and methods. Those now in use are, in fact, old-fashioned, as they are more focused on complete device extraction for case-relevant device identification. A practical approach, a Digital Forensics Triage, tries to quickly collect facts and give essential insight into this circumstance, which could be described as data-rich but information-poor. In time-sensitive scenarios, digital forensics triage approaches can prioritize some electronic gadgets over others based on their significance to the criminal case. The Digital Forensic Laboratories (DFS) make it easier to identify essential gadgets in criminal proceedings when time, significant accumulations, and the accused's confidentiality are critical considerations. Consequently, digital forensics and machine learning techniques allow for the rapid classification of appropriate gadgets despite dipping the quantity of information that has to be adequately studied. This study presents a digital forensic model that may be utilized to build a robotic digital device categorization tool employed in real-world criminal investigations.